Add doc for max value

mindspore/ops/operations/array_ops.py

@@ -653,6 +653,7 @@ class Transpose(PrimitiveWithCheck):
     def check_dtype(self, x, perm):
         validator.check_subclass("x", x, mstype.tensor, self.name)
 
+
 class Unique(Primitive):
     """
     Returns the unique elements of input tensor and also return a tensor containing the index of each value of input
@@ -1672,6 +1673,9 @@ class UnsortedSegmentSum(PrimitiveWithInfer):
     up. Segment_ids does not need to be sorted, and it does not need to cover all values in the entire valid value
     range.
 
+    Note:
+        If the segment_id i is absent in the segment_ids, then output[i] will be filled with 0.
+
     If the sum of the given segment_ids :math:`i` is empty, then :math:`\text{output}[i] = 0`. If the given segment_ids
     is negative, the value will be ignored. 'num_segments' must be equal to the number of different segment_ids.
 
@@ -1751,6 +1755,10 @@ class UnsortedSegmentMin(PrimitiveWithInfer):
           The data type must be int32.
         - **num_segments** (int) - The value spcifies the number of distinct `segment_ids`.
 
+    Note:
+        If the segment_id i is absent in the segment_ids, then output[i] will be filled with
+        the maximum value of the input_x's type.
+
     Outputs:
         Tensor, set the number of `num_segments` as `N`, the shape is :math:`(N, x_2, ..., x_R)`.
 
@@ -1801,6 +1809,10 @@ class UnsortedSegmentMax(PrimitiveWithInfer):
           The data type must be int32.
         - **num_segments** (int) - The value spcifies the number of distinct `segment_ids`.
 
+    Note:
+        If the segment_id i is absent in the segment_ids, then output[i] will be filled with
+        the minimum value of the input_x's type.
+
     Outputs:
         Tensor, set the number of `num_segments` as `N`, the shape is :math:`(N, x_2, ..., x_R)`.
 
@@ -2916,12 +2928,14 @@ class ScatterUpdate(_ScatterOp_Dynamic):
         [[2.0, 1.2, 1.0],
          [3.0, 1.2, 1.0]]
     """
+
     @prim_attr_register
     def __init__(self, use_locking=True):
         """Initialize ScatterUpdate"""
         validator.check_value_type('use_locking', use_locking, [bool], self.name)
         self.init_prim_io_names(inputs=['x', 'indices', 'updates'], outputs=['y'])
 
+
 class ScatterNdUpdate(_ScatterNdOp):
     """
     Updates tensor value by using input indices and value.
@@ -3078,6 +3092,7 @@ class ScatterAdd(_ScatterOp_Dynamic):
         >>> print(output)
         [[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]]
     """
+
     @prim_attr_register
     def __init__(self, use_locking=False):
         """Initialize ScatterAdd"""